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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Research Project #448608

Research Project: Development of AI-Machine Vision Based Automated Robotics Technologies for Microbial Food Safety and Agricultural Applications

Location: Environmental Microbial & Food Safety Laboratory

Project Number: 8042-42000-021-018-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Sep 1, 2025
End Date: Aug 31, 2027

Objective:
Automated robotics technologies integrated with AI-based machine-vision or optical imaging systems are needed to enhance agricultural productivity while ensuring microbial food safety and security for use across diverse agricultural fields from farm to fork. The objectives are to develop multifunctional robotics for applications in controlled environment agriculture to perform labor-intensive tasks, such as planting, inspecting, and culling seedlings with defects or contaminations, to optimize operational efficiency and productivity. In addition, robotics will be implemented as part of an automated surveillance platform development where the AI-machine vision-driven robotics is designed to detect evidence of wildlife intrusion and fecal contamination on produce farm fields, mark the spots, and notify the end-users of the location to mitigate food safety risks.

Approach:
The aim of this project is to develop and validate multifunctional robotics technologies equipped with machine-vision systems for applications in controlled-environment agriculture and in agricultural fields to automate labor-intensive tasks to ensure cost and labor-efficient production of food. For this research, a 6 degrees of freedom (6DoF) robotic arm that can move in six independent directions, coupled with a machine vision system, will be the basis for achieving complex tasks in agricultural applications. AI-based edge-computing will be embedded into the system to control the robotic arm with a machine vision or optical imaging system to allow complex movement to perform labor-intensive tasks and detect defects and contamination on crops that are not discernible to human vision. For the machine-vision system development, multimodal imaging technologies, including color, multispectral, thermal, and stereoscopic 3D imaging, will be considered.